Quantum Machine Learning Solves Complex Many-Body Problems
Seoul National University researchers havedemonstrated the potential of combining quantum experimental data with classical machine learning to tackle complex problems in quantummany-body physics.
The advancement of quantum hardware has enabled the acquisition of data that is impossible to simulate using traditional computers. This has opened up exciting possibilities forusing classical machine learning (ML) algorithms to analyze this data and uncover hidden patterns. This hybrid approach has the potential to expand the range of problems that can besolved effectively compared to using traditional computers alone.
However, the presence of noise in current quantum computers has limited the applicability of this method to only restricted problems. Now, researchers from Seoul National University have extended the applicability of this hybrid approach toaddress challenging problems in many-body physics, such as predicting the ground state properties of a given Hamiltonian and classifying quantum phases.
By conducting various error-mitigation experiments on superconducting quantum hardware with 127 qubits, the researchersmanaged to obtain accurate data from the quantum computer. This allowed them to demonstrate the effectiveness of theoretically proposed classical ML algorithms for systems with up to 44 qubits.
Their findings, published in Nature Communications on August 30, 2024, under the title Machine learning on quantumexperimental data toward solving quantum many-body problems, validate the scalability and efficiency of classical ML algorithms in handling quantum experimental data.
The study’s key findings:
- Researchers successfully extracted accurate data from a 127-qubit superconducting quantum computer using error-mitigation techniques.
- This datawas then used to train classical ML algorithms, demonstrating their effectiveness in solving complex many-body problems.
- The study showed that classical ML algorithms can be applied to systems with up to 44 qubits, highlighting their scalability and potential for tackling even larger quantum systems.
This research represents a significant step forward inutilizing quantum hardware and classical ML algorithms to address fundamental challenges in quantum physics. As quantum hardware continues to improve, this hybrid approach is poised to play a crucial role in unlocking the potential of quantum computers for scientific discovery and technological innovation.
References:
- Kim, J., et al. (2024). Machine learningon quantum experimental data toward solving quantum many-body problems. Nature Communications, 15, 4751.
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